This disclosure relates to methods and systems for performing three-dimensional (3D) mapping of an environment using data collection devices such as a laser scanner.
Reconstructing 3D maps from laser scanners is an under constrained problem because the scanner, which is typically installed on a robotic platform, is constantly moving in an unknown trajectory, and the available sensor data only provides a discretized slice of the reality. Typically, in robotic mapping, a simultaneous localization and mapping (SLAM) is used to construct or update a map of an unknown environment while simultaneously keeping track of a robot's location within it.
Most existing mapping systems use a Light Detection and Ranging (LiDAR, LIDAR or LADAR) sensor that measures distance to a target by illuminating the target with a laser light. However, LiDAR-based 3D mapping suffers some drawbacks. For example, map registrations based on LiDAR data may generate errors, particularly after long continuous scans. Rotational drifts around corners and translational drifts in relatively featureless environments or when transitioning between environments with abrupt feature changes (e.g., from room to hallway or from inside to outside) may cause the majority of the errors. In previous experiments, it was found that up to 60% of scans had visible registration problems such as phantom walls, skewing, open ended loops etc. This requires human intervention to remedy, wherein the operator reviews the data, adjusts the mapping algorithm and flags the scans for re-registration. In another experiment, 25% of all scans using commercial mapping systems failed to generate usable maps.
It is therefore desirable to develop a 3D mapping system that can produce registration with high accuracy from continuous scan for an extended period of time that requires reasonable computation time. This document describes devices and methods that are intended to address at least some issues discussed above and other issues.